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Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series

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  • Alexandros Menelaos Tzortzis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 157 72 Athens, Greece)

  • Sotiris Pelekis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 157 72 Athens, Greece)

  • Evangelos Spiliotis

    (Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 157 72 Athens, Greece)

  • Evangelos Karakolis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 157 72 Athens, Greece)

  • Spiros Mouzakitis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 157 72 Athens, Greece)

  • John Psarras

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 157 72 Athens, Greece)

  • Dimitris Askounis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 157 72 Athens, Greece)

Abstract

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessarily include the target series. In the present study, we investigate the performance of a special case of STLF, namely transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement feed-forward NN model and perform a clustering analysis to identify similar patterns among the load series and enhance TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered.

Suggested Citation

  • Alexandros Menelaos Tzortzis & Sotiris Pelekis & Evangelos Spiliotis & Evangelos Karakolis & Spiros Mouzakitis & John Psarras & Dimitris Askounis, 2023. "Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series," Mathematics, MDPI, vol. 12(1), pages 1-24, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:19-:d:1304656
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    References listed on IDEAS

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    1. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    2. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    3. Yin, Linfei & Xie, Jiaxing, 2021. "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems," Applied Energy, Elsevier, vol. 283(C).
    4. Seung-Min Jung & Sungwoo Park & Seung-Won Jung & Eenjun Hwang, 2020. "Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities," Sustainability, MDPI, vol. 12(16), pages 1-20, August.
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
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